def plotdata(trendselection=None, startdate='2000-01-01', enddate='2018-01-01', trendline=False):
# Handle inputs
startyear = convertdate(startdate, '%Y-%m-%d')
endyear = convertdate(enddate, '%Y-%m-%d')
trendoptions = getoptions(tojson=False)
if trendselection is None: trendselection = trendoptions.keys()[0]
datatype = trendoptions[trendselection]
# Make graph
fig = pl.figure()
fig.add_subplot(111)
thesedata = df.findrows(key=datatype, col='type')
years = thesedata['date']
vals = thesedata['close']
validinds = sc.findinds(pl.logical_and(years>=startyear, years<=endyear))
x = years[validinds]
y = vals[validinds]
pl.plot(x, y)
pl.xlabel('Date')
pl.ylabel('Trend index')
# Add optional trendline
if trendline:
newy = sc.smoothinterp(x, x, y, smoothness=200)
pl.plot(x, newy, lw=3)
# Convert to FE
graphjson = sw.mpld3ify(fig, jsonify=False) # Convert to dict
return graphjson # Return the JSON representation of the Matplotlib figure
def plot_burden(project_id, burdenkey, dosave=True):
''' Plot the disease burden '''
proj = load_project(project_id) # Get the Project object.
burdenset = proj.burden(
key=burdenkey) # Get the burden set that matches burdenset_numindex.
# Create the figures and convert to mpld3
figdicts = []
if burdenset.data:
figs = burdenset.plot()
for fig in figs:
figdict = sw.mpld3ify(fig, jsonify=False)
figdicts.append(figdict)
# Optionally save the figures
if dosave:
filepath = get_path(filename=figures_filename,
username=proj.webapp.username)
sc.savefigs(figs=figs, filetype='singlepdf', filename=filepath)
print('Figures saved to %s' % filepath)
# Return success -- WARNING, hard-coded to 3 graphs!
return {
'graph1': figdicts[0],
'graph2': figdicts[1],
'graph3': figdicts[2],
}
else:
return None # No graphs to make
def computation(seed=0, n=1000):
# Make graph
pl.seed(int(seed))
fig = pl.figure()
ax = fig.add_subplot(111)
xdata = pl.randn(n)
ydata = pl.randn(n)
colors = sc.vectocolor(pl.sqrt(xdata**2+ydata**2))
ax.scatter(xdata, ydata, c=colors)
# Convert to FE
graphjson = sw.mpld3ify(fig, jsonify=False) # Convert to dict
return graphjson # Return the JSON representation of the Matplotlib figure
def plot_packages(project_id, packagekey, dosave=True):
''' Plot the health packages '''
proj = load_project(project_id) # Get the Project object.
packageset = proj.package(
key=packagekey
) # Get the package set that matches packageset_numindex.
# Make the plots
figs = []
fig1 = packageset.plot_spending(which='current')
fig2 = packageset.plot_spending(which='optimized')
fig3 = packageset.plot_dalys(which='current')
fig4 = packageset.plot_dalys(which='optimized')
fig5 = packageset.plot_cascade()
figs.append(fig1)
figs.append(fig2)
figs.append(fig3)
figs.append(fig4)
figs.append(fig5)
figdicts = []
for fig in figs:
figdict = sw.mpld3ify(fig, jsonify=False)
figdicts.append(figdict)
# Optionally save to PDF
if dosave:
filepath = get_path(filename=figures_filename,
username=proj.webapp.username)
sc.savefigs(figs=figs, filetype='singlepdf', filename=filepath)
print('Figures saved to %s' % filepath)
# Return success -- WARNING, should not be hard-coded!
return {
'graph1': figdicts[0],
'graph2': figdicts[1],
'graph3': figdicts[2],
'graph4': figdicts[3],
'graph5': figdicts[4],
}
if __name__ == '__main__':
if 'blank' in torun and doplot:
if doplot:
sw.browser()
if 'browser' in torun:
figs = []
for n in [10, 50]:
fig = pl.figure()
pl.plot(pl.rand(n), pl.rand(n))
figs.append(fig)
barfig = pl.figure()
pl.bar(pl.arange(10), pl.rand(10))
barjson = sw.mpld3ify(barfig)
if doplot:
sw.browser(figs=figs + [barjson])
if 'advanced' in torun:
def make_fig():
fig = pl.figure()
ax = fig.add_subplot(111)
ax.plot([
1,
4,
3,
4,
], label='mish')
ax.plot([8, 4, 3, 2], label='mashed potatoes')